Solving　the　problem　of　water　level　fluctuations　in　small　hydropower　station　systems　is　challenging　under　traditional　industrial　control　methods.　This　difficulty　arises　from　the　system’s　high　nonlinearity　and　the　complexities　involved　in　mechanism　modeling.　To　address　this,　an　improved　neuro-fuzzy　approach　is　proposed.　In　which,　the　multi-head　attention　mechanism　based　long　short-term　memory　network　is　used　to　describe　complex　water　level　change　patterns,　and　the　fuzzy　controller　is　introduced　to　dynamically　adjust　the　control　parameters　to　reduce　water　level　fluctuation.　Simulation-based　on　real　hydropower　station　system　data　is　carried　out,　and　the　superiority　of　the　improved　model　under　complex　dynamic　conditions　is　verified　by　comparing　the　prediction　accuracy　of　different　neural　network　methods　and　the　effects　of　fuzzy　controller　and　traditional　PID　control.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.